CTN Seminar Series Hybrid w ARNI

Bence Olveczky (Harvard University) - June 26, 2026

Location: ZI
Date: June 26, 2026
Time: 11:30am
Zoom: Upon Request @ [email protected]

Title and Abstract: TBD

Andreas Tolias (Standford University) - March 6, 2026

Location: ZI, Kavli Auditorium
Date: March 6, 2026
Time: 11:30am
Zoom: Upon Request @ [email protected]

Title: Foundation models of the brain

Abstract: You … your memories and ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells …’ Crick’s words capture the profound challenge of decrypting the neural code. This challenge has long been hindered by our limited ability to record activity from large neuronal populations under the complex, variable conditions in which brains evolve, and our capacity to model the intricate relationships between stimuli, behaviors, and neural activity. Recent breakthroughs are starting to overcome these barriers. Cutting-edge technologies now enable large-scale recordings, while AI can construct predictive brain models that link stimuli, neural activity, and behavior. These digital twins open the door to limitless in silico experiments, testing theories that are otherwise impossible at scale in living brains. I will discuss our work in creating these digital twins and uncovering neural representation mechanisms, which we validate with closed-loop experiments.

Scott Linderman (Standford University) - January 23, 2026

Scott Linderman

Location: Kavli Auditorium at Zuckerman Institute (9th Floor)
Date: 1/23/2026
Time: 11:30am
Zoom Link: Upon request @ [email protected]

Title: When and How to Parallelize Seemingly Sequential Models

Abstract: Transformers have become the de facto model for sequential data in large part because they are well adapted to modern hardware: At training time, the loss can be evaluated in parallel over the sequence length on GPUs and TPUs. By contrast, evaluating nonlinear recurrent neural networks (RNNs) appears to be an inherently sequential problem. However, recent advances like DEER (arXiv:2309.12252) and DeepPCR (arXiv:2309.16318) have shown that evaluating a nonlinear recursion can be recast as solving a parallelizable optimization problem, and sometimes this approach can yield dramatic speed-ups in wall-clock time. However, the factors that govern the difficulty of these optimization problems remain unclear, limiting the larger adoption of the technique. I will present a recent line of work from my lab that further develops these methods in both theory and practice. We establish a precise relationship between the dynamics of a nonlinear system and the conditioning of its corresponding optimization formulation. We show that the predictability of a system, defined as the degree to which small perturbations in state influence future behavior, impacts the number of optimization steps required for evaluation. In predictable systems, the state trajectory can be computed in O(log2T) time, where T is the sequence length, a major improvement over the conventional sequential approach. In contrast, chaotic or unpredictable systems exhibit poor conditioning, with the consequence that parallel evaluation converges too slowly to be useful. We validate our claims through extensive experiments, with a particular emphasis on parallelizing nonlinear RNNs and Markov chain Monte Carlo (MCMC) algorithms for Bayesian statistics. I will provide practical guidance on when nonlinear dynamical systems can be efficiently parallelized, and highlighting predictability as a key design principle for parallelizable models.

SueYeon Chung (Harvard University) - February 13, 2026

Ann Hermundstad (Janelia Research Campus) - May 8, 2026

Bruno Olshausen (UC Berkeley) - March 15, 2026

Anna Schapiro (University of Pennslyvania) - October 24, 2025

Anna Schapiro

Location: ZI
Date: 10/24/2025
Time: 11:30am
Zoom Link: Upon request @ [email protected]

Title: Learning representations of specifics and generalities over time

Abstract: There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we rapidly learn novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how hippocampal replay shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

Dani Bassett (University of Pennsylvania) - September 19, 2025

Center for Theoretical Neuroscience Seminar Series w ARNI